federated learning

2026-10-12

RESEARCH · 1 SOURCE · arXiv cs.AI

DeFiRank: federated pairwise ranking for governance in decentralized finance

The paper introduces DeFiRank, a federated learning–based pairwise ranking framework that lets multiple institutions collaboratively train a provider-ranking model for DeFi governance without sharing private transaction data. Evaluated on a real-world blockchain transaction dataset, the federated approach attains NDCG@15 ≈ 0.87 (vs. centralized ≈ 0.92) and Kendall τ ≈ 0.70, and yields governance actions including 648 provider promotions and 39 demotions while preserving data privacy.

5.0

2026-09-15

CODING · 1 SOURCE · NVIDIA Developer

Scaling federated learning across Docker, Kubernetes, and Slurm with NVIDIA FLARE

An NVIDIA Developer article describes how NVIDIA FLARE can be used to run and scale federated learning workloads across common orchestration environments — Docker, Kubernetes, and Slurm — offering guidance for moving beyond single-server, few-client setups to larger deployments.

5.0

2026-09-15

RESEARCH · 1 SOURCE · arXiv cs.AI

FedV-KGQA in Practice: Design Lessons and an Interactive Prototype

This arXiv poster reports empirical results for FedV-KGQA, a federated approach to multi-hop question answering over vertically partitioned knowledge graphs where organizations share entity identifiers but keep disjoint relation types. Each silo trains local KG embeddings and a server concatenates silo-specific entity views, anchors the question at a topic entity, and ranks candidates without exchanging raw triples or relation embeddings; experiments show federated fusion recovers most centralized accuracy, anchoring and enrichment matter more than embedding choice, and the cheapest encoder depends on target accuracy; the paper also provides four design lessons and an interactive prototype with released checkpoints.

6.0

2024-03-07

RESEARCH · 1 SOURCE · Google Research

Social learning: Google Research framework for LLMs to teach each other via natural language

Google Research authors propose a "social learning" framework in which teacher LLMs transfer knowledge to a student LLM using natural-language instructions and synthesized examples rather than sharing raw data. Evaluated with PaLM 2‑S on tasks including spam detection, grade‑school math, and text-based question answering, they find that generated examples (e.g., 8–16 per case) often preserve privacy while yielding comparable student performance for most tasks, though spam detection was a notable exception; the paper also proposes quantitative privacy measures for this setting.

7.0